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2021-02-11 13:34:23 -05:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import svm\n",
"import csv\n",
"from sklearn.utils import shuffle\n",
"\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.linear_model import LogisticRegression\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"food_file_path, non_food_file_path = 'food.csv', 'no_food.csv'\n",
"# food_file_path2, non_food_file_path2 = 'food2.csv', 'no_food2.csv'\n",
"food_raw_data, non_food_raw_data = [], []\n",
"# food_raw_data2, non_food_raw_data2 = [], []\n",
"\n",
"train_raw_food, train_raw_non_food = [], []\n",
"test_raw_food, test_raw_non_food = [], []\n",
"\n",
"train_vector_x, train_vector_y, train_vector_num = [], [], []\n",
"test_vector_x, test_vector_y = [], []\n",
"\n",
"useful_tag_list, useful_dict = [], {}\n",
"\n",
"correlation_dict = {}\n",
"\n",
"p_food = 0\n",
"\n",
"\n",
"def get_raw_data():\n",
" global food_raw_data, non_food_raw_data\n",
" \n",
" with open(food_file_path) as f:\n",
" csv_reader = csv.reader(f)\n",
" for row in csv_reader:\n",
" food_raw_data.append(row)\n",
" \n",
" with open(non_food_file_path) as f:\n",
" csv_reader = csv.reader(f)\n",
" for row in csv_reader:\n",
" non_food_raw_data.append(row)\n",
"\n",
"\n",
"\n",
"def get_raw_data_seperate(test_total = False):\n",
" global train_raw_food,train_raw_non_food,\\\n",
" test_raw_food, test_raw_non_food\n",
"\n",
" with open(\"train_food.csv\") as f:\n",
" csv_reader = csv.reader(f)\n",
" for row in csv_reader:\n",
" train_raw_food.append(row)\n",
" with open(\"train_non_food.csv\") as f:\n",
" csv_reader = csv.reader(f)\n",
" for row in csv_reader:\n",
" train_raw_non_food.append(row)\n",
" with open(\"test_food.csv\") as f:\n",
" csv_reader = csv.reader(f)\n",
" for row in csv_reader:\n",
" test_raw_food.append(row)\n",
" if test_total is True:\n",
" with open(\"test_non_food_total.csv\") as f:\n",
" csv_reader = csv.reader(f)\n",
" for row in csv_reader:\n",
" test_raw_non_food.append(row)\n",
" else:\n",
" with open(\"test_non_food.csv\") as f:\n",
" csv_reader = csv.reader(f)\n",
" for row in csv_reader:\n",
" test_raw_non_food.append(row)\n",
"\n",
"\n",
" for i in range(len(train_raw_food)):\n",
" train_raw_food[i] = train_raw_food[i][1:]\n",
" for i in range(len(train_raw_non_food)):\n",
" train_raw_non_food[i] = train_raw_non_food[i][1:]\n",
" for i in range(len(test_raw_food)):\n",
" test_raw_food[i] = test_raw_food[i][1:]\n",
" for i in range(len(test_raw_non_food)):\n",
" test_raw_non_food[i] = test_raw_non_food[i][1:]\n",
" # with open(food_file_path2) as f:\n",
" # csv_reader = csv.reader(f)\n",
" # for row in csv_reader:\n",
" # food_raw_data2.append(row)\n",
" # with open(non_food_file_path2) as f:\n",
" # csv_reader = csv.reader(f)\n",
" # for row in csv_reader:\n",
" # non_food_raw_data2.append(row)\n",
"\n",
"\n",
"def shuffle_raw_data():\n",
" global food_raw_data, non_food_raw_data\n",
" # non_food_raw_data = non_food_raw_data[:15000]\n",
" # non_food_raw_data = non_food_raw_data[:len(food_raw_data)]\n",
" food_raw_data = shuffle(food_raw_data)\n",
" non_food_raw_data = shuffle(non_food_raw_data)\n",
" # non_food_raw_data = non_food_raw_data[:15000]\n",
" non_food_raw_data = non_food_raw_data[:len(food_raw_data)]\n",
" # non_food_raw_data = non_food_raw_data[00000]\n",
"\n",
" \n",
"\n",
"def div_train_test_raw_data(ratio=0.75):\n",
" global food_raw_data, non_food_raw_data, train_raw_food, \\\n",
" train_raw_non_food, test_raw_food, test_raw_non_food, \\\n",
" food_raw_data2, non_food_raw_data2\n",
" # remove some non_food_raw_data\n",
" # non_food_raw_data = non_food_raw_data[:10000]\n",
" train_food_len = int(len(food_raw_data) * ratio)\n",
" train_non_food_len = int(len(non_food_raw_data) * ratio)\n",
" train_raw_food = food_raw_data[0:train_food_len]\n",
" train_raw_non_food = non_food_raw_data[0:train_non_food_len]\n",
" test_raw_food = food_raw_data[train_food_len:]\n",
" test_raw_non_food = non_food_raw_data[train_non_food_len:]\n",
"\n",
" # train_raw_food = food_raw_data\n",
" # test_raw_food = food_raw_data2\n",
"\n",
" # train_raw_non_food = non_food_raw_data\n",
" # test_raw_non_food = non_food_raw_data2\n",
"\n",
"def save_raw_data_train_test():\n",
" global food_raw_data, non_food_raw_data, train_raw_food, \\\n",
" train_raw_non_food, test_raw_food, test_raw_non_food\n",
" with open('train_food.csv', 'w') as f:\n",
" write = csv.writer(f)\n",
" write.writerows(train_raw_food)\n",
" with open('train_non_food.csv', 'w') as f:\n",
" write = csv.writer(f)\n",
" write.writerows(train_raw_non_food )\n",
"\n",
" with open('test_food.csv', 'w') as f:\n",
" write = csv.writer(f)\n",
" write.writerows(test_raw_food )\n",
" with open('test_non_food.csv', 'w') as f:\n",
" write = csv.writer(f)\n",
" write.writerows(test_raw_non_food)\n",
"\n",
" for i in range(len(train_raw_food)):\n",
" train_raw_food[i] = train_raw_food[i][1:]\n",
" for i in range(len(train_raw_non_food)):\n",
" train_raw_non_food[i] = train_raw_non_food[i][1:]\n",
" for i in range(len(test_raw_food)):\n",
" test_raw_food[i] = test_raw_food[i][1:]\n",
" for i in range(len(test_raw_non_food)):\n",
" test_raw_non_food[i] = test_raw_non_food[i][1:]\n",
"\n",
" \n",
"\n",
"\n",
"def count_dict(raw_data, threshold=0.5):\n",
" counter_dict = {} # only collect from train data\n",
" for i in raw_data:\n",
" for j in range(0, len(i) - 1, 2):\n",
" tmp = str(i[j]).strip()\n",
" if float(i[j + 1]) > threshold:\n",
" if tmp not in counter_dict:\n",
" counter_dict[tmp] = 1\n",
" else:\n",
" counter_dict[tmp] += 1\n",
" else:\n",
" # if tmp not in counter_dict:\n",
" # counter_dict[tmp] = 0\n",
" pass\n",
" return counter_dict\n",
"\n",
"\n",
"def get_use_tag(use_all=False, threshold=0.5):\n",
" global useful_tag_list, food_raw_data, non_food_raw_data, useful_dict\n",
" useful_tag_list, useful_dict = [], {}\n",
" food_tag_dict = count_dict(train_raw_food)\n",
" non_food_tag_dict = count_dict(train_raw_non_food)\n",
"\n",
" if use_all:\n",
" for i in non_food_tag_dict.keys():\n",
" if i not in food_tag_dict.keys():\n",
" food_tag_dict[i] = non_food_tag_dict[i]\n",
" else:\n",
" food_tag_dict[i] += non_food_tag_dict[i]\n",
" # food_tag_dict.update(non_food_tag_dict)\n",
"\n",
" appear_times = 0\n",
" appear_list = []\n",
" for i in food_tag_dict.keys():\n",
" appear_times += food_tag_dict[i]\n",
" appear_list.append(food_tag_dict[i])\n",
" appear_list.sort(reverse=True)\n",
" useful_bound = int(appear_times * threshold)\n",
" bound = 0\n",
" pre_sum = 0\n",
" for i in range(len(appear_list)):\n",
" pre_sum += appear_list[i]\n",
" if pre_sum > useful_bound:\n",
" bound = appear_list[i]\n",
" break\n",
" for i in food_tag_dict.keys():\n",
" if food_tag_dict[i] > bound:\n",
" useful_tag_list.append(i)\n",
" counter = 0\n",
" for i in useful_tag_list:\n",
" useful_dict[i] = counter\n",
" counter += 1\n",
"\n",
"\n",
"def get_correlation():\n",
" global train_raw_food, correlation_dict\n",
" food_tag_dict = count_dict(train_raw_food)\n",
" merged_dict = count_dict(train_raw_non_food)\n",
"\n",
" for i in food_tag_dict.keys():\n",
" if i not in merged_dict.keys():\n",
" merged_dict[i] = food_tag_dict[i]\n",
" else:\n",
" merged_dict[i] += food_tag_dict[i]\n",
"\n",
" for i in food_tag_dict.keys():\n",
" if i not in correlation_dict.keys():\n",
" # correlation_dict[i] = food_tag_dict[i] / len(food_raw_data)\n",
" correlation_dict[i] = food_tag_dict[i] / len(train_raw_food)\n",
" # correlation_dict[i] = food_tag_dict[i] / merged_dict[i]\n",
" else:\n",
" print(\"error in get correlation function\")\n",
"\n",
"\n",
"def construct_train_test_set():\n",
" global train_raw_food, train_raw_non_food, test_raw_food, \\\n",
" test_raw_non_food, train_vector_x, train_vector_y, \\\n",
" test_vector_x, test_vector_y, train_vector_num\n",
" train_vector_x, train_vector_y, train_vector_num = [], [], []\n",
" test_vector_x, test_vector_y = [], []\n",
" vector_x = []\n",
" vector_y = []\n",
" for i in train_raw_food:\n",
" tmp = [0 for i in range(len(useful_tag_list))]\n",
" for j in range(0, len(i) - 1, 2):\n",
" if i[j] in useful_dict.keys():\n",
" tmp[useful_dict[i[j]]] = float(\n",
" i[j + 1]) * correlation_dict[i[j]] + p_food * (1 - float(i[j + 1]))\n",
" else:\n",
" # TODO: should be changed to random probability\n",
" pass\n",
" vector_x.append(tmp)\n",
" vector_y.append(\"food\")\n",
" train_vector_num.append(1)\n",
"\n",
" for i in train_raw_non_food:\n",
" tmp = [0 for i in range(len(useful_tag_list))]\n",
" for j in range(0, len(i) - 1, 2):\n",
" if i[j] in useful_dict.keys():\n",
" tmp[useful_dict[i[j]]] = float(\n",
" i[j + 1]) * correlation_dict[i[j]] + p_food * (1 - float(i[j + 1]))\n",
" else:\n",
" # TODO: should be changed to random probability\n",
" pass\n",
" vector_x.append(tmp)\n",
" vector_y.append(\"no food\")\n",
" train_vector_num.append(-1)\n",
"\n",
" train_vector_x, train_vector_y = vector_x, vector_y\n",
"\n",
" vector_x, vector_y = [], []\n",
"\n",
" for i in test_raw_food:\n",
" tmp = [0 for i in range(len(useful_tag_list))]\n",
" for j in range(0, len(i) - 1, 2):\n",
" if i[j] in useful_dict.keys():\n",
" tmp[useful_dict[i[j]]] = float(\n",
" i[j + 1]) * correlation_dict[i[j]] + p_food * (1 - float(i[j + 1]))\n",
" else:\n",
" # TODO: should be changed to random probability\n",
" pass\n",
" vector_x.append(tmp)\n",
" vector_y.append(\"food\")\n",
"\n",
" for i in test_raw_non_food:\n",
" tmp = [0 for i in range(len(useful_tag_list))]\n",
" for j in range(0, len(i) - 1, 2):\n",
" if i[j] in useful_dict.keys():\n",
" tmp[useful_dict[i[j]]] = float(\n",
" i[j + 1]) * correlation_dict[i[j]] + p_food * (1 - float(i[j + 1]))\n",
" else:\n",
" # TODO: should be changed to random probability\n",
" pass\n",
" vector_x.append(tmp)\n",
" vector_y.append(\"no food\")\n",
" test_vector_x, test_vector_y = vector_x, vector_y\n",
"\n",
"\n",
"def confision_matrix(ground_true, predict, print_result=False):\n",
" TP, FP, FN, TN = 0, 0, 0, 0\n",
" for i in range(len(ground_true)):\n",
" if ground_true[i] == \"food\" and predict[i] == \"food\":\n",
" TP += 1\n",
" elif ground_true[i] == \"no food\" and predict[i] == \"food\":\n",
" FP += 1\n",
" elif ground_true[i] == \"food\" and predict[i] == \"no food\":\n",
" FN += 1\n",
" elif ground_true[i] == \"no food\" and predict[i] == \"no food\":\n",
" TN += 1\n",
" TPR = TP / (TP + FN)\n",
" FPR = FP / (FP + TN)\n",
" if TP + FP != 0:\n",
" PPV = TP/(TP + FP) # precision\n",
" else:\n",
" PPV = 0\n",
" if print_result:\n",
" print(\"TP: \", TP, \"FN: \", FN, \"TN: \", TN, \"FP :\", FP)\n",
" print(TP, \" \", FN, \" \", TN, \" \", FP)\n",
" print(\"Sensitivity = \", TP/(TP+FN), end=\" \")\n",
" print(\"Specificity = \", TN/(TN+FP), \"burden: \", (TP + FP) / (TP+TN+FN+FP) )\n",
" # print(\"Precision = \", TP/(TP+FP), end=\" \")\n",
" # print(\"Accuracy = \", (TP + TN)/(TP+TN+FN+FP))\n",
" return TPR, FPR, PPV\n",
"\n",
"\n",
"def clarifai_result():\n",
" global test_raw_food, test_raw_non_food\n",
" TPR_list = []\n",
" FPR_list = []\n",
" for k in range(10):\n",
" TP, FP, FN, TN = 0, 0, 0, 0\n",
" ratio = k / 10\n",
" # print(ratio)\n",
" for i in test_raw_food:\n",
" have = False\n",
" for j in range(len(i)):\n",
" if i[j] == \"food\" and float(i[j + 1]) > ratio:\n",
" have = True\n",
" if not have:\n",
" FN += 1\n",
" else:\n",
" TP += 1\n",
"\n",
" for i in test_raw_non_food:\n",
" have = False\n",
" for j in range(len(i)):\n",
" if i[j] == \"food\" and float(i[j + 1]) > ratio:\n",
" have = True\n",
" if not have:\n",
" TN += 1\n",
" else:\n",
" FP += 1\n",
"\n",
" TPR = TP / (TP + FN)\n",
" FPR = FP / (FP + TN)\n",
" TPR_list.append(TPR)\n",
" FPR_list.append(FPR)\n",
" #if k == 9 or k == 8 or k == 7 or k ==6:\n",
" if k == 7 or k == 6 or k == 5 or k == 4:\n",
"# plt.scatter([FPR], [TPR], marker='o', c='green')\n",
" print(\"TP: \", TP, \"FN: \", FN, \"TN: \", TN, \"FP :\", FP, \"Sensitivity: \",TP / (TP + FN),\"Specificity: \",TN / (TN + FP),\"burden: \", (TP + FP) / (TP+TN+FN+FP) )\n",
" print(TP, \", \", FN, \", \", TN, \", \", FP)\n",
" \n",
" # print(\"TRP :\", TPR)\n",
" # print(\"FPR :\", FPR)\n",
" return TPR_list, FPR_list\n",
" #\n",
" # print(\"clarify result**********\")\n",
" # print(\"TP: \", TP, \"FN: \", FN, \"TN: \", TN, \"FP :\", FP)\n",
" # print(\"Sensitivity = \", TP / (TP + FN), end=\" \")\n",
" # print(\"Specificity = \", TN / (TN + FP))\n",
" # print(\"Precision = \", TP / (TP + FP), end=\" \")\n",
" # print(\"Accuracy = \", (TP + TN) / (TP + TN + FN + FP))\n",
" # print(\"burden = \", (TP + FP) / (TP +TN+FN+FP))\n",
" # plt.scatter([1 - 0.789866667, 1 - 0.684, 1 - 0.55786, 1-0.4512], [0.584493042, 0.666003976, 0.753479125, 0.833664679], marker='o', c='green')\n",
"\n",
"\n",
"def get_p_food_before_balance():\n",
" # food_num_ori = 0\n",
" # with open(\"#food_ori.csv\") as f:\n",
" # csv_reader = csv.reader(f)\n",
" # for row in csv_reader:\n",
" # food_num_ori += 1\n",
"\n",
" # no_food_num_ori = 0\n",
" # with open(\"#no food_ori.csv\") as f:\n",
" # csv_reader = csv.reader(f)\n",
" # for row in csv_reader:\n",
" # no_food_num_ori += 1\n",
" # p_food = food_num_ori / no_food_num_ori\n",
" # return p_food\n",
" # p_food = len(train_raw_food) / (len(train_raw_food) + len(train_raw_non_food))\n",
" # return p_food\n",
" pass\n",
"\n",
"\n",
"def init(use_all=True):\n",
" global p_food, train_raw_food, train_raw_non_food\n",
" get_raw_data()\n",
" shuffle_raw_data()\n",
" div_train_test_raw_data(0.75)\n",
" save_raw_data_train_test()\n",
" clarifai_result()\n",
" p_food = len(train_raw_food) / (len(train_raw_food) + len(train_raw_non_food))\n",
" get_correlation()\n",
" get_use_tag(use_all)\n",
" construct_train_test_set()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"scrolled": false,
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"6182\n",
"TP: 1756 FN: 305 TN: 6491 FP : 6310 Sensitivity: 0.8520135856380397 Specificity: 0.5070697601749863 burden: 0.5427264163638811\n",
"1756 , 305 , 6491 , 6310\n",
"TP: 1630 FN: 431 TN: 7929 FP : 4872 Sensitivity: 0.7908782144590005 Specificity: 0.6194047340051558 burden: 0.4374915892881173\n",
"1630 , 431 , 7929 , 4872\n",
"TP: 1474 FN: 587 TN: 9281 FP : 3520 Sensitivity: 0.7151868025230471 Specificity: 0.7250214826966643 burden: 0.33602476113578256\n",
"1474 , 587 , 9281 , 3520\n",
"TP: 1296 FN: 765 TN: 10400 FP : 2401 Sensitivity: 0.62882096069869 Specificity: 0.812436528396219 burden: 0.24875521464136724\n",
"1296 , 765 , 10400 , 2401\n",
"init done\n"
]
}
],
"source": [
"# init training data testing data and required parameter\n",
"\n",
"# p_food, train_raw_food, train_raw_non_food\n",
"# get_raw_data()\n",
"# shuffle_raw_data()\n",
"# div_train_test_raw_data(0.75)\n",
"# save_raw_data_train_test()\n",
"\n",
"get_raw_data_seperate(test_total = False)\n",
"print(len(train_raw_food))\n",
"\n",
"TPR_list, FPR_list = clarifai_result()\n",
"p_food = len(train_raw_food) / (len(train_raw_food) + len(train_raw_non_food))\n",
"get_correlation()\n",
"get_use_tag(use_all = True)\n",
"construct_train_test_set()\n",
"print('init done')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"trainning svm kernel done\n"
]
}
],
"source": [
"clf = svm.SVC(kernel='poly') # order = 3(default)\n",
"clf.fit(train_vector_x, train_vector_y)\n",
"print('trainning svm kernel done')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n\n food 0.90 0.92 0.91 6182\n no food 0.91 0.90 0.90 6182\n\n accuracy 0.91 12364\n macro avg 0.91 0.91 0.91 12364\nweighted avg 0.91 0.91 0.91 12364\n\nTP: 5663 FN: 519 TN: 5534 FP : 648\n5663 519 5534 648\nSensitivity = 0.9160465868650922 Specificity = 0.8951795535425429 burden: 0.5104335166612747\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(0.9160465868650922, 0.10482044645745714, 0.8973221359530977)"
]
},
"metadata": {},
"execution_count": 4
}
],
"source": [
"### svm training dataset report\n",
"svm_predict = clf.predict(train_vector_x)\n",
"print(classification_report(train_vector_y, svm_predict))\n",
"confision_matrix(train_vector_y, svm_predict, print_result=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"scrolled": true
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"***********************testing set report ********************************\n",
"TP: 1750 FN: 311 TN: 10545 FP : 2256\n",
"1750 311 10545 2256\n",
"Sensitivity = 0.849102377486657 Specificity = 0.8237637684555894 burden: 0.26954649441528733\n"
]
}
],
"source": [
"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
"print('***********************testing set report ********************************')\n",
"svm_predict = clf.predict(test_vector_x)\n",
"TPR, FPR, PPV = confision_matrix(test_vector_y, svm_predict, print_result=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['food' 'food' 'food' ... 'no food' 'no food' 'no food']\n",
"2061\n",
"12801\n",
"food\n",
"no food\n",
"done\n"
]
}
],
"source": [
"# get FP and FN\n",
"# print(test_vector_y)\n",
"print(svm_predict)\n",
"print(len(test_raw_food))\n",
"print(len(test_raw_non_food))\n",
"print(test_vector_y[2060])\n",
"print(test_vector_y[2061])\n",
"\n",
"FN = []\n",
"FP = []\n",
"for i in range(len(svm_predict)):\n",
" if svm_predict[i] != test_vector_y[i]:\n",
" if i < 2061:\n",
" FN.append(i)\n",
" else:\n",
" FP.append(i-len(test_raw_food))\n",
"# print(FN)\n",
"# print(FP)\n",
"test_raw_food, test_raw_non_food = [], []\n",
"with open(\"test_food.csv\") as f:\n",
" csv_reader = csv.reader(f)\n",
" for row in csv_reader:\n",
" test_raw_food.append(row)\n",
"\n",
"with open(\"test_non_food.csv\") as f:\n",
" csv_reader = csv.reader(f)\n",
" for row in csv_reader:\n",
" test_raw_non_food.append(row)\n",
"with open(\"FN.txt\", 'w') as f:\n",
" for i in FN:\n",
" f.write(test_raw_food[i][0]+\"\\n\")\n",
"with open(\"./FP.txt\", 'w') as f:\n",
" for i in FP:\n",
" f.write(test_raw_non_food[i][0]+\"\\n\")\n",
"print(\"done\")\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"##%\n",
"\n",
"# linera discriminant analysis\n",
"vector_x_np = np.array(train_vector_x)\n",
"vector_y_np = np.array(train_vector_num)\n",
"\n",
"\n",
"lda = LinearDiscriminantAnalysis().fit(vector_x_np, vector_y_np)\n",
"predict_y_prob = lda.predict_proba(test_vector_x)\n",
"predict_y = lda.predict(test_vector_x)\n",
"\n",
"lda = LinearDiscriminantAnalysis().fit(vector_x_np, vector_y_np)\n",
"predict_y_prob = lda.predict_proba(test_vector_x)\n",
"predict_y = lda.predict(test_vector_x)\n",
"# print(predict_y_prob)\n",
"# print(predict_y)\n",
"TPR_logistic, FPR_logistic = [], []\n",
"step = 200\n",
"for threshold in range(0,step):\n",
" predict_y_str = []\n",
" for i in predict_y_prob:\n",
" if i[0] >= threshold/step:\n",
" predict_y_str.append(\"no food\")\n",
" else:\n",
" predict_y_str.append(\"food\")\n",
" TPR_tmp, FPR_tmp, PPV_tmp = confision_matrix(test_vector_y, predict_y_str, print_result=False)\n",
" # TPR_logistic.append(PPV_tmp)\n",
" # FPR_logistic.append(TPR_tmp)\n",
" TPR_logistic.append(TPR_tmp)\n",
" FPR_logistic.append(FPR_tmp)\n",
"TPR_jia = [0.984958758, 0.899563319, 0.712760796, 0.463367297]\n",
"FPR_jia = [0.988125928, 0.930864776, 0.785094914, 0.573783298]\n",
"plt.scatter(FPR_logistic,TPR_logistic,marker='.',c='blue', s=5)\n",
"# plt.scatter(FPR_jia, TPR_jia, marker='.', c='#E68600')\n",
"plt.scatter([FPR], [TPR], marker='*', c='red')\n",
"# plt.scatter([TPR], [PPV], marker='*', c='red')\n",
"plt.text(FPR, TPR+0.05, '({:.2f}, {:.2f})'.format(FPR, TPR))\n",
"# plt.scatter(FPR_list, TPR_list, marker='.', c='green', s=0.5)\n",
"plt.xlabel('1 − Specificity')\n",
"plt.ylabel('Sensitivity')\n",
"\n",
"# plt.scatter([FPR], [TPR], marker='*', c='red')\n",
"# plt.scatter(FPR_list[4:8], TPR_list[4:8], marker='.', c='green')\n",
"# plt.plot([0, 1], [0, 1], linestyle='--', color='magenta')\n",
"plt.xlim(0, 1)\n",
"plt.ylim(0, 1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"#######################################\n",
"# this block is for logistic regreesion\n",
"#######################################\n",
"# reg = LogisticRegression(max_iter=300).fit(vector_x_np, vector_y_np)\n",
"# predict_y_prob = reg.predict_proba(test_vector_x)\n",
"# predict_y = reg.predict(test_vector_x)\n",
"# TPR_logistic, FPR_logistic = [], []\n",
"# step = 200\n",
"# for threshold in range(0,step):\n",
"# predict_y_str = []\n",
"# for i in predict_y_prob:\n",
"# if i[0] >= threshold/step:\n",
"# predict_y_str.append(\"no food\")\n",
"# else:\n",
"# predict_y_str.append(\"food\")\n",
"# TPR_tmp, FPR_tmp = confision_matrix(test_vector_y, predict_y_str, print_result=False)\n",
"# TPR_logistic.append(TPR_tmp)\n",
"# FPR_logistic.append(FPR_tmp)\n",
"\n",
"# plt.scatter(FPR_logistic,TPR_logistic,marker='.',c='blue')\n",
"\n",
"# plt.scatter([FPR], [TPR], marker='*', c='red')\n",
"# plt.scatter(FPR_list, TPR_list, marker='.', c='green', s=0.005)\n",
"\n",
"# plt.plot([0, 1], [0, 1], linestyle='--', color='magenta')\n",
"# plt.xlim(0, 1)\n",
"# plt.ylim(0, 1)\n",
"# plt.show()\n"
]
}
],
"metadata": {
"kernelspec": {
"name": "pycharm-d87231c7",
"language": "python",
"display_name": "PyCharm (all_food_no_food.new)"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1-final"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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